Human–robot shared control for humanoid manipulator trajectory planning

Purpose: This paper aims to propose an intuitive shared control strategy to control a humanoid manipulator that can fully combine the advantages of humans and machines to produce a stronger intelligent form. Design/methodology/approach: The working space of an operator’s arm and that of a manipulator are matched, and a genetic algorithm that limits the position of the manipulator’s elbow joint is used to find the optimal solution. Then, the mapping of the operator’s action to that of manipulators is realized. The controls of the human and robot are integrated. First, the current action of the operator is input. Second, the target object is predicted according to the maximum entropy hypothesis. Third, the joint angle of the manipulator is interpolated based on time. Finally, the confidence and weight of the current moment are calculated. Findings: The modified weight adjustment method is the optimal way to adjust the weight during the task. In terms of time and accuracy, the experimental results of single target obstacle avoidance grabbing and multi-target predictive grabbing show that the shared control mode can provide full play to the advantages of humans and robots to accomplish the target task faster and more accurately than the control merely by a human or robot on its own. Originality/value: A flexible and highly anthropomorphic human–robot action mapping method is proposed, which provides operator decisions in the shared control process. The shared control between human and the robot is realized, and it enhances the rapidity and intelligence, paving a new way for a novel human–robot collaboration.